DOC: Enhancing documentation of the Dimension Reduction chapter

Examples/DimensionReduction/ICAExample.cxx

@@ -31,10 +31,15 @@
 //
 // This example illustrates the use of the
 // \doxygen{otb}{FastICAImageFilter}.
-// This filter computes a Principal Component Analysis using an
-// efficient method based on the inner product in order to compute the
-// covariance matrix.
+// This filter computes a Fast Independant Components Analysis transform.
 //
+// Like Principal Components Analysis, FastICA computes a set of
+// orthogonal linear combinations, but the criterion of Fast ICA is
+// different: instead of maximizing variance, it tries to maximize
+// stastistical independance between components. In Fast ICA,
+// statistical independance is mesured by evaluating non-Gaussianity
+// of the components, and the maximization is done in an iterative way.
+
 // The first step required to use this filter is to include its header file.
 //
 // Software Guide : EndLatex
@@ -144,7 +149,7 @@ int main(int argc, char* argv[])
   // Software Guide : EndCodeSnippet
   // Software Guide : BeginLatex
   //
-  // We finally plug the pipeline and trigger the PCA computation with
+  // We finally plug the pipeline and trigger the ICA computation with
   // the method \code{Update()} of the writer.
   //
   // Software Guide : EndLatex
@@ -159,7 +164,7 @@ int main(int argc, char* argv[])
   // Software Guide : BeginLatex
   //
   // \doxygen{otb}{FastICAImageFilter} allows also to compute inverse
-  // transformation from PCA coefficients. In reverse mode, the
+  // transformation from ICA coefficients. In reverse mode, the
   // covariance matrix or the transformation matrix
   // (which may not be square) has to be given.
   //
@@ -173,7 +178,8 @@ int main(int argc, char* argv[])
   invFilter->SetMeanValues( FastICAfilter->GetMeanValues() );
   invFilter->SetStdDevValues( FastICAfilter->GetStdDevValues() );
   invFilter->SetTransformationMatrix( FastICAfilter->GetTransformationMatrix() );
-  invFilter->SetPCATransformationMatrix( FastICAfilter->GetPCATransformationMatrix() );
+  invFilter->SetPCATransformationMatrix( 
+                            FastICAfilter->GetPCATransformationMatrix() );
   invFilter->SetInput(FastICAfilter->GetOutput());
     
   WriterType::Pointer invWriter = WriterType::New();

Examples/DimensionReduction/MNFExample.cxx

@@ -30,11 +30,22 @@
 // Software Guide : BeginLatex
 //
 // This example illustrates the use of the
-// \doxygen{otb}{MNFImageFilter}.
-// This filter computes a Principal Component Analysis using an
+// \doxygen{otb}{MNFImageFilter}.  This filter computes a Minimum
+// Noise Fraction transform \cite{nielsen2011kernel} using an
 // efficient method based on the inner product in order to compute the
 // covariance matrix.
 //
+// The Minimum Noise Fraction transform is a sequence of two Principal
+// Components Analysis transform. The first transform is based on an
+// estimated covariance matrix of the noise, and intends to whiten the
+// input image (noise with unit variance and no correlation between
+// bands). 
+//
+// The second Principal Components Analysis is then applied to the
+// noise-whitened image, giving the Minimum Noise Fraction transform.
+//
+// In this implementation, noise is estimated from a local window.
+//
 // The first step required to use this filter is to include its header file.
 //
 // Software Guide : EndLatex
@@ -100,7 +111,7 @@ int main(int argc, char* argv[])
   // Software Guide : EndLatex
 
   // SoftwareGuide : BeginCodeSnippet  
-  typedef otb::LocalActivityVectorImageFilter< ImageType, ImageType > NoiseFilterType;
+  typedef otb::LocalActivityVectorImageFilter<ImageType,ImageType> NoiseFilterType;
   // SoftwareGuide : EndCodeSnippet
 
 
@@ -167,7 +178,7 @@ int main(int argc, char* argv[])
   // Software Guide : EndCodeSnippet
   // Software Guide : BeginLatex
   //
-  // We finally plug the pipeline and trigger the PCA computation with
+  // We finally plug the pipeline and trigger the MNF computation with
   // the method \code{Update()} of the writer.
   //
   // Software Guide : EndLatex
@@ -182,7 +193,7 @@ int main(int argc, char* argv[])
   // Software Guide : BeginLatex
   //
   // \doxygen{otb}{MNFImageFilter} allows also to compute inverse
-  // transformation from PCA coefficients. In reverse mode, the
+  // transformation from MNF coefficients. In reverse mode, the
   // covariance matrix or the transformation matrix
   // (which may not be square) has to be given.
   //

Examples/DimensionReduction/MaximumAutocorrelationFactor.cxx

@@ -28,7 +28,16 @@
 
 //  Software Guide : BeginLatex 
 // This example illustrates the class
-// \doxygen{otb}{MaximumAutocorrelationFactorImageFilter} ...
+// \doxygen{otb}{MaximumAutocorrelationFactorImageFilter}, which
+// performs a Maximum Autocorrelation Factor transform \cite{nielsen2011kernel}. Like
+// PCA, MAF tries to find a set of orthogonal linear transform, but
+// the criterion to maximize is the auto-correlation rather than the
+// variance.
+//
+// Auto-correlation is the correlation between the component and a
+// unitary shifted version of the component.
+// 
+// Please note that the inverse transform is not implemented yet.
 //
 // We start by including the corresponding header file.
 //

Examples/DimensionReduction/NAPCAExample.cxx

@@ -100,7 +100,7 @@ int main(int argc, char* argv[])
   // Software Guide : EndLatex
 
   // SoftwareGuide : BeginCodeSnippet  
-  typedef otb::LocalActivityVectorImageFilter< ImageType, ImageType > NoiseFilterType;
+  typedef otb::LocalActivityVectorImageFilter<ImageType,ImageType> NoiseFilterType;
   // SoftwareGuide : EndCodeSnippet
 
 
@@ -167,7 +167,7 @@ int main(int argc, char* argv[])
   // Software Guide : EndCodeSnippet
   // Software Guide : BeginLatex
   //
-  // We finally plug the pipeline and trigger the PCA computation with
+  // We finally plug the pipeline and trigger the NA-PCA computation with
   // the method \code{Update()} of the writer.
   //
   // Software Guide : EndLatex
@@ -182,7 +182,7 @@ int main(int argc, char* argv[])
   // Software Guide : BeginLatex
   //
   // \doxygen{otb}{NAPCAImageFilter} allows also to compute inverse
-  // transformation from PCA coefficients. In reverse mode, the
+  // transformation from NA-PCA coefficients. In reverse mode, the
   // covariance matrix or the transformation matrix
   // (which may not be square) has to be given.
   //
@@ -209,7 +209,7 @@ int main(int argc, char* argv[])
   
   //  Software Guide : BeginLatex
   // Figure~\ref{fig:NAPCA_FILTER} shows the result of applying forward
-  // and reverse NAPCA transformation to a 8 bands Wordlview2 image.
+  // and reverse NA-PCA transformation to a 8 bands Wordlview2 image.
   // \begin{figure}
   // \center
   // \includegraphics[width=0.32\textwidth]{napca-input-pretty.eps}